Am I right in assuming that modules in sklearn extend functionality in scipy?
If that is the case, it seems there is an explicit link to a version of scipy with atlas within the decomposition module. Rather than linking to atlas in the build of sklearn, switching to the atlas version of scipy resolved the issue. Best, Andrew On Oct 18, 2012, at 8:48 AM, Gael Varoquaux <[email protected]> wrote: > On Thu, Oct 18, 2012 at 07:02:41AM +0100, Andreas Mueller wrote: >> In principle I think it should be possible to run with the standard numpy as >> well. > > One thing to keep in mind is that accelerate leads to crashes after doing > a fork. Thus if you want to do multiprocessing/joblib-based parallel > computing, it should not be used. > > In general, I would recommend to rely rather on something like atlas when > doing scientific computing with Python. > > Still, we should be able to build correctly :$ > > Gaël > > ------------------------------------------------------------------------------ > Everyone hates slow websites. So do we. > Make your web apps faster with AppDynamics > Download AppDynamics Lite for free today: > http://p.sf.net/sfu/appdyn_sfd2d_oct > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://p.sf.net/sfu/appdyn_sfd2d_oct _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
